added my data ML test

JR_day2_tensorflow/lidoML_TF.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jan 23 09:59:50 2020
+
+@author: tanu
+"""
+#%%
+# LIDO ML: tensorflow
+#%%
+
+from sklearn.datasets import load_boston
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import MinMaxScaler
+import jrpytensorflow
+import tensorflow as tf
+
+import matplotlib.pyplot as plt
+
+boston = load_boston()
+#X, y = boston.data, boston.target
+
+
+from sklearn.model_selection import train_test_split
+
+from sklearn.datasets import load_digits
+
+digits = load_digits()
+
+X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size = 0.2
+)
+#%%
+# P1
+X,y = jrpytensorflow.datasets.load_circles() 
+
+plt.figure()
+plt.scatter(X[:,0], X[:,1], c = y, edgecolor = 'black')
+
+#2)
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size = 0.2
+)
+
+
+preprocess = Pipeline(
+        steps = [
+              ('rescale', MinMaxScaler())
+              ]
+)
+
+preprocess.fit(X_train)
+
+X_train = preprocess.transform(X_train)
+X_test = preprocess.transform(X_test)
+
+import tensorflow as tf
+logModel = tf.keras.models.Sequential([
+        tf.keras.layers.Dense(1, activation = 'sigmoid')
+        
+])
+
+logModel.compile(optimizer = 'sgd',
+                 loss = 'binary_crossentropy')
+history = logModel.fit(X, y, epochs = 100)
+
+logModel.summary()
+
+
+#4) predicted probability: for X
+model(X) # tf object
+model.predict(X) # array
+
+
+model(X).numpy().ravel() > 0.5
+
+sum(model(X).numpy().ravel() > 0.5)
+#101
+
+sum( ( model(X).numpy().ravel() > 0.5) == y)
+#97
+
+# 4*) predicted probability: for X_test
+
+logModel.compile(optimizer = 'sgd',
+                 loss = 'binary_crossentropy')
+history = logModel.fit(X_test, y_test, epochs = 100)
+
+logModel.summary()
+
+
+
+model(X_test) # tf object
+model.predict(X_test) # array
+
+
+model(X_test).numpy().ravel() > 0.5
+
+sum( model(X_test).numpy().ravel() > 0.5) 
+#22
+
+sum((model(X_test).numpy().ravel() > 0.5)  == y_test)
+#21
+
+#%%
+# Practical 2
+#%%
+
+from tensorflow import keras
+
+def smallModel():
+    model = tf.keras.models.Sequential([
+            tf.keras.layers.Dense(20, activation = 'relu'),
+            tf.keras.layers.Dense(10, activation = 'relu'),
+            tf.keras.layers.Dense(1, activation = 'softmax')
+            ])
+    model.compile(optimizer = 'sgd',
+                  loss = 'binary_crossentropy',
+                  metrics = ['accuracy'])
+
+    return model
+
+model = smallModel()
+model.summary()
+
+